# Importing the necessary libraries

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Assigning name to the dataset and calling the dataset
dataset1 = pd.read_csv("C:\\Users\\HP\\Downloads\\GL_files\\project_oct_new\\parkinsons.csv")

dataset1  # Printing the dataset

# for features in dataset1.columns:
#     if dataset1[features].dtype == 'object':
#         dataset1[features] = pd.Categorical(dataset1[features]).codes

dataset2= dataset1.drop(['name'],axis =1)

dataset2.shape  # Analysis on the dimension of the dataset

(195, 23)

dataset2.dtypes  # Analysing the dataset datatypes 

# There are int and float datatypes 
# No object datatypes are being present

MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object

dataset2.isnull().sum()     #Code to check whether any missing values are present on the dataset
                            #  and to get it treated

MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64

dataset2.head(10)   # inital 10 head datas of the Parkinsons dataset to understand the value distribution

dataset2.describe()    # describe function could provide the distribution of the values of the dataset to study the mean, median and outliers presented on the dataset

figure1=plt.figure(figsize = [20,20])        # plt.figure for the figure creation
ax= figure1.gca()                            # This code will get axes instance on the current figure matching the keyword argws
dataset2.hist(ax=ax)                         # The figure procedures are passed as the arguments to the histogram plot

                                             # The histogram plot is being applied to the dataset1
plt.show()                                   # For showing all the plots, this function being called

C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3296: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
  exec(code_obj, self.user_global_ns, self.user_ns)

dataset2.isnull().any()  # any() will return the boolean values of the columns on the dataset1

MDVP:Fo(Hz)         False
MDVP:Fhi(Hz)        False
MDVP:Flo(Hz)        False
MDVP:Jitter(%)      False
MDVP:Jitter(Abs)    False
MDVP:RAP            False
MDVP:PPQ            False
Jitter:DDP          False
MDVP:Shimmer        False
MDVP:Shimmer(dB)    False
Shimmer:APQ3        False
Shimmer:APQ5        False
MDVP:APQ            False
Shimmer:DDA         False
NHR                 False
HNR                 False
status              False
RPDE                False
DFA                 False
spread1             False
spread2             False
D2                  False
PPE                 False
dtype: bool

dataset2.columns

Index(['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE'],
      dtype='object')

dataset2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 23 columns):
MDVP:Fo(Hz)         195 non-null float64
MDVP:Fhi(Hz)        195 non-null float64
MDVP:Flo(Hz)        195 non-null float64
MDVP:Jitter(%)      195 non-null float64
MDVP:Jitter(Abs)    195 non-null float64
MDVP:RAP            195 non-null float64
MDVP:PPQ            195 non-null float64
Jitter:DDP          195 non-null float64
MDVP:Shimmer        195 non-null float64
MDVP:Shimmer(dB)    195 non-null float64
Shimmer:APQ3        195 non-null float64
Shimmer:APQ5        195 non-null float64
MDVP:APQ            195 non-null float64
Shimmer:DDA         195 non-null float64
NHR                 195 non-null float64
HNR                 195 non-null float64
status              195 non-null int64
RPDE                195 non-null float64
DFA                 195 non-null float64
spread1             195 non-null float64
spread2             195 non-null float64
D2                  195 non-null float64
PPE                 195 non-null float64
dtypes: float64(22), int64(1)
memory usage: 35.1 KB

dataset2["status"].unique()

array([1, 0], dtype=int64)

dataset2.status.value_counts()

# dataset1["status"].value_counts() --> this is also prooduce the above results

1    147
0     48
Name: status, dtype: int64

dataset2[dataset2['status']==1].describe()  #Study for the people with chance of Parkinsons disease

dataset2[dataset2['status']==0].describe() #Study for the people without of Parkinsons disease

dataset2.groupby(by=['status']).count()  # Another way of finding the unique values on the python
print(dataset2.groupby(by=['status']).mean())

# Groupby() is the concept used,when dealing with large types of the values (binding the rows into groups depending in the features on the dataset)

        MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \
status                                                            
0        181.937771    223.636750    145.207292        0.003866   
1        145.180762    188.441463    106.893558        0.006989   

        MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \
status                                                                   
0               0.000023  0.001925  0.002056    0.005776      0.017615   
1               0.000051  0.003757  0.003900    0.011273      0.033658   

        MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA       NHR        HNR  \
status                    ...                                               
0               0.162958  ...  0.013305     0.028511  0.011483  24.678750   
1               0.321204  ...  0.027600     0.053027  0.029211  20.974048   

            RPDE       DFA   spread1   spread2        D2       PPE  
status                                                              
0       0.442552  0.695716 -6.759264  0.160292  2.154491  0.123017  
1       0.516816  0.725408 -5.333420  0.248133  2.456058  0.233828  

[2 rows x 22 columns]

dataset1.groupby(by=['status']).mean()   # Mean study based on the status

dataset2.groupby(by=['status']).median() # Median study based on the status

#Study of the dataset through the graph
sns.pairplot(data= dataset2)

<seaborn.axisgrid.PairGrid at 0xd9b2828>

sns.pairplot(data= dataset2,hue = "status")

C:\Users\HP\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:488: RuntimeWarning: invalid value encountered in true_divide
  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)
C:\Users\HP\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars
  FAC1 = 2*(np.pi*bw/RANGE)**2

<seaborn.axisgrid.PairGrid at 0x211900f0>

from sklearn.model_selection import train_test_split
X = dataset2.drop(['status'], axis =1)
#X
#print(X.shape)

y= dataset2['status']
#print(y.shape)                    #(195,)


#y = dataset1[['status','HNR']]    
#print(y.shape)                   # (195, 2)

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.30)

X_train

X_test

y_train

51     0
158    1
139    1
145    1
118    1
191    0
113    1
73     1
168    0
0      1
178    1
123    1
42     0
57     1
44     0
93     1
45     0
34     0
108    1
97     1
172    0
82     1
11     1
134    1
189    0
127    1
78     1
129    1
185    0
146    1
      ..
64     0
102    1
91     1
61     0
14     1
95     1
67     1
157    1
111    1
138    1
33     0
52     0
75     1
101    1
122    1
144    1
46     0
71     1
124    1
16     1
77     1
3      1
86     1
19     1
68     1
54     1
130    1
104    1
187    0
22     1
Name: status, Length: 136, dtype: int64

from sklearn.tree import DecisionTreeClassifier
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',random_state =1)   

# Model creation using the entropy method

dt_model1.fit(X_train,y_train)

# Fitting the train values inside the fit method

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=1,
            splitter='best')

# calculating the accuracy value on the test and train datas

print(dt_model1.score(X_train,y_train))    # Using the train values
print(dt_model1.score(X_test,y_test))      # Using the test values  
# 86% accuravy value is being achieved  without applying any parameter conditions

1.0
0.8305084745762712

dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth = 12)   
dt_model1.fit(X_train,y_train)

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=12,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

print(dt_model1.score(X_train,y_train))    # Using the train values
#Training error is zero in other words 

print(dt_model1.score(X_test,y_test))      # Using the test values  
# 86% of the test records are correctly classified

1.0
0.8305084745762712

# Regularizing part of the Decision Tree


for  values in range(1,15):
    dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth=values)
    dt_model1.fit(X_train,y_train)
    print("Training value accuracy_score for :",dt_model1.score(X_train,y_train),"Testing Value accuracy_value for :",dt_model1.score(X_test,y_test))
    
    
    # For the max_depth of 4, the accuracy values are found to be good, avoiding the overfitting condition

Training value accuracy_score for : 0.8676470588235294 Testing Value accuracy_value for : 0.847457627118644
Training value accuracy_score for : 0.875 Testing Value accuracy_value for : 0.847457627118644
Training value accuracy_score for : 0.875 Testing Value accuracy_value for : 0.847457627118644
Training value accuracy_score for : 0.8897058823529411 Testing Value accuracy_value for : 0.7966101694915254
Training value accuracy_score for : 0.9411764705882353 Testing Value accuracy_value for : 0.8135593220338984
Training value accuracy_score for : 0.9779411764705882 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712

from sklearn.feature_extraction.text import CountVectorizer

y_predict = dt_model1.predict(X_test)

dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth = 4)   
dt_model1.fit(X_train,y_train)

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

print(dt_model1.score(X_train,y_train))    

print(dt_model1.score(X_test,y_test))

0.8897058823529411
0.7966101694915254

from sklearn import metrics
print(metrics.confusion_matrix(y_test,y_predict))

# Dataframe creation for the heatmap 

dataframe_for_heatmap = pd.DataFrame(metrics.confusion_matrix(y_test,y_predict),index=['Healthy','Parkinsons'],columns=['Healthy','Parkinsons'])
#print(type(dataframe_for_heatmap))
print(dataframe_for_heatmap)

[[ 9  5]
 [ 5 40]]
            Healthy  Parkinsons
Healthy           9           5
Parkinsons        5          40

dataframe_for_heatmap

# Heatmap design
sns.heatmap(data = dataframe_for_heatmap,annot = True)
plt.xlabel('Acutal')
plt.ylabel('Predicted')
plt.show()

print(pd.DataFrame(dt_model1.feature_importances_, columns=['Imp'],index =X_train.columns ))

                       Imp
MDVP:Fo(Hz)       0.000000
MDVP:Fhi(Hz)      0.000000
MDVP:Flo(Hz)      0.251038
MDVP:Jitter(%)    0.000000
MDVP:Jitter(Abs)  0.000000
MDVP:RAP          0.071047
MDVP:PPQ          0.000000
Jitter:DDP        0.000000
MDVP:Shimmer      0.000000
MDVP:Shimmer(dB)  0.000000
Shimmer:APQ3      0.000000
Shimmer:APQ5      0.000000
MDVP:APQ          0.000000
Shimmer:DDA       0.000000
NHR               0.000000
HNR               0.000000
RPDE              0.000000
DFA               0.000000
spread1           0.237025
spread2           0.000000
D2                0.000000
PPE               0.440890

# Using min_sample_leaf as theonly parameter on the decision tree classifier 
for values in range(1,15):
    dt_model1 = DecisionTreeClassifier(criterion = 'entropy',min_samples_leaf=values)   
    dt_model1.fit(X_train,y_train)
    print("Training value accuracy_score:",dt_model1.score(X_train,y_train),"Testing Value accuracy_value:",dt_model1.score(X_test,y_test))
    
    
    
# At the values of min_samples_leaf=6, the accuracy values for the training and  test railings are found to be nearly matching and similar

Training value accuracy_score: 1.0 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9852941176470589 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9779411764705882 Testing Value accuracy_value: 0.8135593220338984
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712
Training value accuracy_score: 0.9558823529411765 Testing Value accuracy_value: 0.864406779661017
Training value accuracy_score: 0.9191176470588235 Testing Value accuracy_value: 0.8135593220338984
Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644
Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644
Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644

dt_model1 = DecisionTreeClassifier(criterion = 'entropy',min_samples_leaf=6)   
dt_model1.fit(X_train,y_train)

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=6, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

print(dt_model1.score(X_train,y_train))    
print(dt_model1.score(X_test,y_test)) 
print(metrics.confusion_matrix(y_test,y_predict))

0.9705882352941176
0.8305084745762712
[[ 9  5]
 [ 5 40]]

Using Ensemble Random Forest Classifier¶

from sklearn.ensemble import RandomForestClassifier
model2 = RandomForestClassifier(n_estimators=5)
model2.fit(X_train,y_train)

# 5 instances creation

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

model2.score(X_train,y_train)

0.9926470588235294

# For training set 
test_predict = model2.predict(X_test)
model2.score(X_test,y_test)

# Everytime, when this technique is given run, they will not produce accurate results, they will keep on changing for every run
# Hence random_state seeding is required  to produce the more accurate results
# For selecting that value, the below code follows

0.8983050847457628

from sklearn.ensemble import RandomForestClassifier
for x in range(1,14):
    model2 = RandomForestClassifier(n_estimators=x,random_state=252)
    model2.fit(X_train,y_train)
    print("Training value accuracy_score  :",model2.score(X_train,y_train))
    test_predict = model2.predict(X_test)
    print("Testing Value accuracy_value  :",model2.score(X_test,y_test))
    
    
    
# 1) For the n_estimators for value = 5, we are getting the 'best accuracy score' when compared to other values, since
# on the syntax of the RandomForestClassifier(n_estimators =10), the general value is 10

Training value accuracy_score  : 0.8897058823529411
Testing Value accuracy_value  : 0.7966101694915254
Training value accuracy_score  : 0.8970588235294118
Testing Value accuracy_value  : 0.8305084745762712
Training value accuracy_score  : 0.9558823529411765
Testing Value accuracy_value  : 0.864406779661017
Training value accuracy_score  : 0.9852941176470589
Testing Value accuracy_value  : 0.9152542372881356
Training value accuracy_score  : 0.9779411764705882
Testing Value accuracy_value  : 0.9322033898305084
Training value accuracy_score  : 0.9705882352941176
Testing Value accuracy_value  : 0.864406779661017
Training value accuracy_score  : 0.9926470588235294
Testing Value accuracy_value  : 0.9152542372881356
Training value accuracy_score  : 0.9779411764705882
Testing Value accuracy_value  : 0.9322033898305084
Training value accuracy_score  : 0.9852941176470589
Testing Value accuracy_value  : 0.9152542372881356
Training value accuracy_score  : 0.9852941176470589
Testing Value accuracy_value  : 0.9322033898305084
Training value accuracy_score  : 0.9926470588235294
Testing Value accuracy_value  : 0.8983050847457628
Training value accuracy_score  : 1.0
Testing Value accuracy_value  : 0.9152542372881356
Training value accuracy_score  : 1.0
Testing Value accuracy_value  : 0.9152542372881356

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
5	phon_R01_S01_6	120.552	131.162	113.787	0.00968	0.00008	0.00463	0.00750	0.01388	0.04701	...	0.06985	0.01222	21.378	1	0.415564	0.825069	-4.242867	0.299111	2.187560	0.357775
6	phon_R01_S02_1	120.267	137.244	114.820	0.00333	0.00003	0.00155	0.00202	0.00466	0.01608	...	0.02337	0.00607	24.886	1	0.596040	0.764112	-5.634322	0.257682	1.854785	0.211756
7	phon_R01_S02_2	107.332	113.840	104.315	0.00290	0.00003	0.00144	0.00182	0.00431	0.01567	...	0.02487	0.00344	26.892	1	0.637420	0.763262	-6.167603	0.183721	2.064693	0.163755
8	phon_R01_S02_3	95.730	132.068	91.754	0.00551	0.00006	0.00293	0.00332	0.00880	0.02093	...	0.03218	0.01070	21.812	1	0.615551	0.773587	-5.498678	0.327769	2.322511	0.231571
9	phon_R01_S02_4	95.056	120.103	91.226	0.00532	0.00006	0.00268	0.00332	0.00803	0.02838	...	0.04324	0.01022	21.862	1	0.547037	0.798463	-5.011879	0.325996	2.432792	0.271362
10	phon_R01_S02_5	88.333	112.240	84.072	0.00505	0.00006	0.00254	0.00330	0.00763	0.02143	...	0.03237	0.01166	21.118	1	0.611137	0.776156	-5.249770	0.391002	2.407313	0.249740
11	phon_R01_S02_6	91.904	115.871	86.292	0.00540	0.00006	0.00281	0.00336	0.00844	0.02752	...	0.04272	0.01141	21.414	1	0.583390	0.792520	-4.960234	0.363566	2.642476	0.275931
12	phon_R01_S04_1	136.926	159.866	131.276	0.00293	0.00002	0.00118	0.00153	0.00355	0.01259	...	0.01968	0.00581	25.703	1	0.460600	0.646846	-6.547148	0.152813	2.041277	0.138512
13	phon_R01_S04_2	139.173	179.139	76.556	0.00390	0.00003	0.00165	0.00208	0.00496	0.01642	...	0.02184	0.01041	24.889	1	0.430166	0.665833	-5.660217	0.254989	2.519422	0.199889
14	phon_R01_S04_3	152.845	163.305	75.836	0.00294	0.00002	0.00121	0.00149	0.00364	0.01828	...	0.03191	0.00609	24.922	1	0.474791	0.654027	-6.105098	0.203653	2.125618	0.170100
15	phon_R01_S04_4	142.167	217.455	83.159	0.00369	0.00003	0.00157	0.00203	0.00471	0.01503	...	0.02316	0.00839	25.175	1	0.565924	0.658245	-5.340115	0.210185	2.205546	0.234589
16	phon_R01_S04_5	144.188	349.259	82.764	0.00544	0.00004	0.00211	0.00292	0.00632	0.02047	...	0.02908	0.01859	22.333	1	0.567380	0.644692	-5.440040	0.239764	2.264501	0.218164
17	phon_R01_S04_6	168.778	232.181	75.603	0.00718	0.00004	0.00284	0.00387	0.00853	0.03327	...	0.04322	0.02919	20.376	1	0.631099	0.605417	-2.931070	0.434326	3.007463	0.430788
18	phon_R01_S05_1	153.046	175.829	68.623	0.00742	0.00005	0.00364	0.00432	0.01092	0.05517	...	0.07413	0.03160	17.280	1	0.665318	0.719467	-3.949079	0.357870	3.109010	0.377429
19	phon_R01_S05_2	156.405	189.398	142.822	0.00768	0.00005	0.00372	0.00399	0.01116	0.03995	...	0.05164	0.03365	17.153	1	0.649554	0.686080	-4.554466	0.340176	2.856676	0.322111
20	phon_R01_S05_3	153.848	165.738	65.782	0.00840	0.00005	0.00428	0.00450	0.01285	0.03810	...	0.05000	0.03871	17.536	1	0.660125	0.704087	-4.095442	0.262564	2.739710	0.365391
21	phon_R01_S05_4	153.880	172.860	78.128	0.00480	0.00003	0.00232	0.00267	0.00696	0.04137	...	0.06062	0.01849	19.493	1	0.629017	0.698951	-5.186960	0.237622	2.557536	0.259765
22	phon_R01_S05_5	167.930	193.221	79.068	0.00442	0.00003	0.00220	0.00247	0.00661	0.04351	...	0.06685	0.01280	22.468	1	0.619060	0.679834	-4.330956	0.262384	2.916777	0.285695
23	phon_R01_S05_6	173.917	192.735	86.180	0.00476	0.00003	0.00221	0.00258	0.00663	0.04192	...	0.06562	0.01840	20.422	1	0.537264	0.686894	-5.248776	0.210279	2.547508	0.253556
24	phon_R01_S06_1	163.656	200.841	76.779	0.00742	0.00005	0.00380	0.00390	0.01140	0.01659	...	0.02214	0.01778	23.831	1	0.397937	0.732479	-5.557447	0.220890	2.692176	0.215961
25	phon_R01_S06_2	104.400	206.002	77.968	0.00633	0.00006	0.00316	0.00375	0.00948	0.03767	...	0.05197	0.02887	22.066	1	0.522746	0.737948	-5.571843	0.236853	2.846369	0.219514
26	phon_R01_S06_3	171.041	208.313	75.501	0.00455	0.00003	0.00250	0.00234	0.00750	0.01966	...	0.02666	0.01095	25.908	1	0.418622	0.720916	-6.183590	0.226278	2.589702	0.147403
27	phon_R01_S06_4	146.845	208.701	81.737	0.00496	0.00003	0.00250	0.00275	0.00749	0.01919	...	0.02650	0.01328	25.119	1	0.358773	0.726652	-6.271690	0.196102	2.314209	0.162999
28	phon_R01_S06_5	155.358	227.383	80.055	0.00310	0.00002	0.00159	0.00176	0.00476	0.01718	...	0.02307	0.00677	25.970	1	0.470478	0.676258	-7.120925	0.279789	2.241742	0.108514
29	phon_R01_S06_6	162.568	198.346	77.630	0.00502	0.00003	0.00280	0.00253	0.00841	0.01791	...	0.02380	0.01170	25.678	1	0.427785	0.723797	-6.635729	0.209866	1.957961	0.135242
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
165	phon_R01_S42_1	236.200	244.663	102.137	0.00277	0.00001	0.00154	0.00153	0.00462	0.02448	...	0.04231	0.00620	24.078	0	0.469928	0.628232	-6.816086	0.172270	2.235197	0.119652
166	phon_R01_S42_2	237.323	243.709	229.256	0.00303	0.00001	0.00173	0.00159	0.00519	0.01242	...	0.02089	0.00533	24.679	0	0.384868	0.626710	-7.018057	0.176316	1.852402	0.091604
167	phon_R01_S42_3	260.105	264.919	237.303	0.00339	0.00001	0.00205	0.00186	0.00616	0.02030	...	0.03557	0.00910	21.083	0	0.440988	0.628058	-7.517934	0.160414	1.881767	0.075587
168	phon_R01_S42_4	197.569	217.627	90.794	0.00803	0.00004	0.00490	0.00448	0.01470	0.02177	...	0.03836	0.01337	19.269	0	0.372222	0.725216	-5.736781	0.164529	2.882450	0.202879
169	phon_R01_S42_5	240.301	245.135	219.783	0.00517	0.00002	0.00316	0.00283	0.00949	0.02018	...	0.03529	0.00965	21.020	0	0.371837	0.646167	-7.169701	0.073298	2.266432	0.100881
170	phon_R01_S42_6	244.990	272.210	239.170	0.00451	0.00002	0.00279	0.00237	0.00837	0.01897	...	0.03253	0.01049	21.528	0	0.522812	0.646818	-7.304500	0.171088	2.095237	0.096220
171	phon_R01_S43_1	112.547	133.374	105.715	0.00355	0.00003	0.00166	0.00190	0.00499	0.01358	...	0.01992	0.00435	26.436	0	0.413295	0.756700	-6.323531	0.218885	2.193412	0.160376
172	phon_R01_S43_2	110.739	113.597	100.139	0.00356	0.00003	0.00170	0.00200	0.00510	0.01484	...	0.02261	0.00430	26.550	0	0.369090	0.776158	-6.085567	0.192375	1.889002	0.174152
173	phon_R01_S43_3	113.715	116.443	96.913	0.00349	0.00003	0.00171	0.00203	0.00514	0.01472	...	0.02245	0.00478	26.547	0	0.380253	0.766700	-5.943501	0.192150	1.852542	0.179677
174	phon_R01_S43_4	117.004	144.466	99.923	0.00353	0.00003	0.00176	0.00218	0.00528	0.01657	...	0.02643	0.00590	25.445	0	0.387482	0.756482	-6.012559	0.229298	1.872946	0.163118
175	phon_R01_S43_5	115.380	123.109	108.634	0.00332	0.00003	0.00160	0.00199	0.00480	0.01503	...	0.02436	0.00401	26.005	0	0.405991	0.761255	-5.966779	0.197938	1.974857	0.184067
176	phon_R01_S43_6	116.388	129.038	108.970	0.00346	0.00003	0.00169	0.00213	0.00507	0.01725	...	0.02623	0.00415	26.143	0	0.361232	0.763242	-6.016891	0.109256	2.004719	0.174429
177	phon_R01_S44_1	151.737	190.204	129.859	0.00314	0.00002	0.00135	0.00162	0.00406	0.01469	...	0.02184	0.00570	24.151	1	0.396610	0.745957	-6.486822	0.197919	2.449763	0.132703
178	phon_R01_S44_2	148.790	158.359	138.990	0.00309	0.00002	0.00152	0.00186	0.00456	0.01574	...	0.02518	0.00488	24.412	1	0.402591	0.762508	-6.311987	0.182459	2.251553	0.160306
179	phon_R01_S44_3	148.143	155.982	135.041	0.00392	0.00003	0.00204	0.00231	0.00612	0.01450	...	0.02175	0.00540	23.683	1	0.398499	0.778349	-5.711205	0.240875	2.845109	0.192730
180	phon_R01_S44_4	150.440	163.441	144.736	0.00396	0.00003	0.00206	0.00233	0.00619	0.02551	...	0.03964	0.00611	23.133	1	0.352396	0.759320	-6.261446	0.183218	2.264226	0.144105
181	phon_R01_S44_5	148.462	161.078	141.998	0.00397	0.00003	0.00202	0.00235	0.00605	0.01831	...	0.02849	0.00639	22.866	1	0.408598	0.768845	-5.704053	0.216204	2.679185	0.197710
182	phon_R01_S44_6	149.818	163.417	144.786	0.00336	0.00002	0.00174	0.00198	0.00521	0.02145	...	0.03464	0.00595	23.008	1	0.329577	0.757180	-6.277170	0.109397	2.209021	0.156368
183	phon_R01_S49_1	117.226	123.925	106.656	0.00417	0.00004	0.00186	0.00270	0.00558	0.01909	...	0.02592	0.00955	23.079	0	0.603515	0.669565	-5.619070	0.191576	2.027228	0.215724
184	phon_R01_S49_2	116.848	217.552	99.503	0.00531	0.00005	0.00260	0.00346	0.00780	0.01795	...	0.02429	0.01179	22.085	0	0.663842	0.656516	-5.198864	0.206768	2.120412	0.252404
185	phon_R01_S49_3	116.286	177.291	96.983	0.00314	0.00003	0.00134	0.00192	0.00403	0.01564	...	0.02001	0.00737	24.199	0	0.598515	0.654331	-5.592584	0.133917	2.058658	0.214346
186	phon_R01_S49_4	116.556	592.030	86.228	0.00496	0.00004	0.00254	0.00263	0.00762	0.01660	...	0.02460	0.01397	23.958	0	0.566424	0.667654	-6.431119	0.153310	2.161936	0.120605
187	phon_R01_S49_5	116.342	581.289	94.246	0.00267	0.00002	0.00115	0.00148	0.00345	0.01300	...	0.01892	0.00680	25.023	0	0.528485	0.663884	-6.359018	0.116636	2.152083	0.138868
188	phon_R01_S49_6	114.563	119.167	86.647	0.00327	0.00003	0.00146	0.00184	0.00439	0.01185	...	0.01672	0.00703	24.775	0	0.555303	0.659132	-6.710219	0.149694	1.913990	0.121777
189	phon_R01_S50_1	201.774	262.707	78.228	0.00694	0.00003	0.00412	0.00396	0.01235	0.02574	...	0.04363	0.04441	19.368	0	0.508479	0.683761	-6.934474	0.159890	2.316346	0.112838
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
5	120.552	131.162	113.787	0.00968	0.00008	0.00463	0.00750	0.01388	0.04701	0.456	...	0.06985	0.01222	21.378	1	0.415564	0.825069	-4.242867	0.299111	2.187560	0.357775
6	120.267	137.244	114.820	0.00333	0.00003	0.00155	0.00202	0.00466	0.01608	0.140	...	0.02337	0.00607	24.886	1	0.596040	0.764112	-5.634322	0.257682	1.854785	0.211756
7	107.332	113.840	104.315	0.00290	0.00003	0.00144	0.00182	0.00431	0.01567	0.134	...	0.02487	0.00344	26.892	1	0.637420	0.763262	-6.167603	0.183721	2.064693	0.163755
8	95.730	132.068	91.754	0.00551	0.00006	0.00293	0.00332	0.00880	0.02093	0.191	...	0.03218	0.01070	21.812	1	0.615551	0.773587	-5.498678	0.327769	2.322511	0.231571
9	95.056	120.103	91.226	0.00532	0.00006	0.00268	0.00332	0.00803	0.02838	0.255	...	0.04324	0.01022	21.862	1	0.547037	0.798463	-5.011879	0.325996	2.432792	0.271362

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	...	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000
mean	154.228641	197.104918	116.324631	0.006220	0.000044	0.003306	0.003446	0.009920	0.029709	0.282251	...	0.046993	0.024847	21.885974	0.753846	0.498536	0.718099	-5.684397	0.226510	2.381826	0.206552
std	41.390065	91.491548	43.521413	0.004848	0.000035	0.002968	0.002759	0.008903	0.018857	0.194877	...	0.030459	0.040418	4.425764	0.431878	0.103942	0.055336	1.090208	0.083406	0.382799	0.090119
min	88.333000	102.145000	65.476000	0.001680	0.000007	0.000680	0.000920	0.002040	0.009540	0.085000	...	0.013640	0.000650	8.441000	0.000000	0.256570	0.574282	-7.964984	0.006274	1.423287	0.044539
25%	117.572000	134.862500	84.291000	0.003460	0.000020	0.001660	0.001860	0.004985	0.016505	0.148500	...	0.024735	0.005925	19.198000	1.000000	0.421306	0.674758	-6.450096	0.174351	2.099125	0.137451
50%	148.790000	175.829000	104.315000	0.004940	0.000030	0.002500	0.002690	0.007490	0.022970	0.221000	...	0.038360	0.011660	22.085000	1.000000	0.495954	0.722254	-5.720868	0.218885	2.361532	0.194052
75%	182.769000	224.205500	140.018500	0.007365	0.000060	0.003835	0.003955	0.011505	0.037885	0.350000	...	0.060795	0.025640	25.075500	1.000000	0.587562	0.761881	-5.046192	0.279234	2.636456	0.252980
max	260.105000	592.030000	239.170000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	33.047000	1.000000	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000	...	147.000000	147.000000	147.000000	147.0	147.000000	147.000000	147.000000	147.000000	147.000000	147.000000
mean	145.180762	188.441463	106.893558	0.006989	0.000051	0.003757	0.003900	0.011273	0.033658	0.321204	...	0.053027	0.029211	20.974048	1.0	0.516816	0.725408	-5.333420	0.248133	2.456058	0.233828
std	32.348050	88.339180	32.274358	0.005240	0.000037	0.003241	0.002998	0.009724	0.019970	0.207798	...	0.032391	0.044447	4.339143	0.0	0.101254	0.054786	0.970792	0.077809	0.375742	0.084271
min	88.333000	102.145000	65.476000	0.001680	0.000010	0.000680	0.000920	0.002040	0.010220	0.090000	...	0.013640	0.002310	8.441000	1.0	0.263654	0.574282	-7.120925	0.063412	1.765957	0.093193
25%	117.572000	133.776500	80.875500	0.004005	0.000030	0.002030	0.002190	0.006085	0.018295	0.168000	...	0.027400	0.008445	18.782000	1.0	0.439064	0.685569	-6.038300	0.199507	2.180933	0.170103
50%	145.174000	163.335000	99.770000	0.005440	0.000040	0.002840	0.003140	0.008530	0.028380	0.263000	...	0.044510	0.016580	21.414000	1.0	0.530529	0.726652	-5.440040	0.240875	2.439597	0.222716
75%	170.071000	207.160500	129.240000	0.007670	0.000060	0.004100	0.004360	0.012300	0.042525	0.394500	...	0.068455	0.027960	24.164500	1.0	0.604573	0.764868	-4.664067	0.303660	2.668479	0.274397
max	223.361000	588.518000	199.020000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	29.928000	1.0	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000	...	48.000000	48.000000	48.00000	48.0	48.000000	48.000000	48.000000	48.000000	48.000000	48.000000
mean	181.937771	223.636750	145.207292	0.003866	0.000023	0.001925	0.002056	0.005776	0.017615	0.162958	...	0.028511	0.011483	24.67875	0.0	0.442552	0.695716	-6.759264	0.160292	2.154491	0.123017
std	52.731067	96.727067	58.757070	0.002055	0.000015	0.001066	0.000943	0.003199	0.005544	0.057822	...	0.010368	0.019088	3.43454	0.0	0.092199	0.051346	0.642782	0.062982	0.310269	0.044820
min	110.739000	113.597000	74.287000	0.001780	0.000007	0.000920	0.001060	0.002760	0.009540	0.085000	...	0.014030	0.000650	17.88300	0.0	0.256570	0.626710	-7.964984	0.006274	1.423287	0.044539
25%	120.947500	139.413250	98.243750	0.002655	0.000010	0.001332	0.001480	0.003998	0.014475	0.129000	...	0.022060	0.004188	22.99325	0.0	0.372126	0.654291	-7.257665	0.120623	1.974217	0.094658
50%	198.996000	231.161500	113.938500	0.003355	0.000025	0.001625	0.001775	0.004875	0.016705	0.154000	...	0.026330	0.004825	24.99700	0.0	0.435368	0.682527	-6.826448	0.167356	2.129510	0.115119
75%	229.077000	251.239250	199.183000	0.004530	0.000030	0.001907	0.002228	0.005725	0.020210	0.189250	...	0.034540	0.009213	26.13925	0.0	0.507748	0.742284	-6.350146	0.193766	2.339487	0.147761
max	260.105000	592.030000	239.170000	0.013600	0.000080	0.006240	0.005640	0.018730	0.040870	0.405000	...	0.070080	0.107150	33.04700	0.0	0.663842	0.785714	-5.198864	0.291954	2.882450	0.252404

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
status
0	198.996	231.1615	113.9385	0.003355	0.000025	0.001625	0.001775	0.004875	0.016705	0.154	...	0.013015	0.02633	0.004825	24.997	0.435368	0.682527	-6.826448	0.167356	2.129510	0.115119
1	145.174	163.3350	99.7700	0.005440	0.000040	0.002840	0.003140	0.008530	0.028380	0.263	...	0.021570	0.04451	0.016580	21.414	0.530529	0.726652	-5.440040	0.240875	2.439597	0.222716

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
51	126.344	134.231	112.773	0.00448	0.000040	0.00131	0.00169	0.00393	0.02033	0.185	...	0.01614	0.03429	0.00474	25.030	0.507504	0.760361	-6.689151	0.291954	2.431854	0.105993
158	126.144	154.284	97.543	0.00975	0.000080	0.00593	0.00454	0.01778	0.02852	0.266	...	0.02157	0.04499	0.03828	21.534	0.635015	0.627337	-5.070096	0.280091	2.892300	0.249703
139	116.150	131.731	109.815	0.00381	0.000030	0.00181	0.00232	0.00542	0.03026	0.267	...	0.02770	0.04543	0.01827	18.801	0.624811	0.696049	-5.866357	0.233070	2.445646	0.184985
145	223.361	263.872	87.638	0.00352	0.000020	0.00169	0.00188	0.00506	0.02536	0.225	...	0.01909	0.04137	0.01493	20.366	0.566849	0.574282	-5.456811	0.345238	2.840556	0.232861
118	178.285	442.824	82.063	0.00462	0.000030	0.00157	0.00194	0.00472	0.01279	0.129	...	0.01151	0.01851	0.00856	25.020	0.470422	0.655239	-4.913137	0.393056	2.816781	0.251972
191	209.516	253.017	89.488	0.00564	0.000030	0.00331	0.00292	0.00994	0.02751	0.263	...	0.01879	0.04812	0.01810	19.147	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
113	210.141	232.706	185.258	0.00534	0.000030	0.00321	0.00280	0.00964	0.01680	0.149	...	0.01301	0.02583	0.00620	23.671	0.441097	0.722254	-5.963040	0.250283	2.489191	0.177807
73	112.014	588.518	107.024	0.00533	0.000050	0.00268	0.00329	0.00805	0.02448	0.226	...	0.01956	0.04120	0.00623	24.178	0.509127	0.789532	-5.389129	0.306636	1.928708	0.225461
168	197.569	217.627	90.794	0.00803	0.000040	0.00490	0.00448	0.01470	0.02177	0.189	...	0.01439	0.03836	0.01337	19.269	0.372222	0.725216	-5.736781	0.164529	2.882450	0.202879
0	119.992	157.302	74.997	0.00784	0.000070	0.00370	0.00554	0.01109	0.04374	0.426	...	0.02971	0.06545	0.02211	21.033	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
178	148.790	158.359	138.990	0.00309	0.000020	0.00152	0.00186	0.00456	0.01574	0.142	...	0.01309	0.02518	0.00488	24.412	0.402591	0.762508	-6.311987	0.182459	2.251553	0.160306
123	182.018	197.173	79.187	0.00842	0.000050	0.00506	0.00449	0.01517	0.02503	0.231	...	0.01931	0.04115	0.01813	18.784	0.589956	0.732903	-5.445140	0.142466	2.174306	0.215558
42	237.226	247.326	225.227	0.00298	0.000010	0.00169	0.00182	0.00507	0.01752	0.164	...	0.01133	0.03104	0.00740	22.736	0.305062	0.654172	-7.310550	0.098648	2.416838	0.095032
57	117.274	129.916	110.402	0.00752	0.000060	0.00299	0.00469	0.00898	0.02293	0.221	...	0.01948	0.03568	0.00681	22.817	0.530529	0.817756	-4.608260	0.290024	2.021591	0.314464
44	243.439	250.912	232.435	0.00210	0.000009	0.00109	0.00137	0.00327	0.01419	0.126	...	0.01033	0.02330	0.00454	25.368	0.438296	0.635285	-7.057869	0.091608	2.330716	0.091470
93	152.125	161.469	76.596	0.00382	0.000030	0.00191	0.00226	0.00574	0.05925	0.637	...	0.04398	0.10024	0.01211	20.969	0.447456	0.697790	-6.152551	0.173520	2.080121	0.160809
45	242.852	255.034	227.911	0.00225	0.000009	0.00117	0.00139	0.00350	0.01494	0.134	...	0.01014	0.02542	0.00476	25.032	0.431285	0.638928	-6.995820	0.102083	2.365800	0.102706
34	203.184	211.526	196.160	0.00178	0.000009	0.00094	0.00106	0.00283	0.00958	0.085	...	0.00726	0.01403	0.00065	33.047	0.340068	0.741899	-7.964984	0.163519	1.423287	0.044539
108	151.989	157.339	132.857	0.00174	0.000010	0.00075	0.00096	0.00225	0.01024	0.093	...	0.00993	0.01364	0.00238	29.928	0.311369	0.676066	-6.739151	0.160686	2.296873	0.115130
97	125.036	143.946	116.187	0.01280	0.000100	0.00743	0.00623	0.02228	0.03886	0.342	...	0.03088	0.06406	0.08151	15.338	0.629574	0.714485	-4.020042	0.265315	2.671825	0.340623
172	110.739	113.597	100.139	0.00356	0.000030	0.00170	0.00200	0.00510	0.01484	0.133	...	0.01285	0.02261	0.00430	26.550	0.369090	0.776158	-6.085567	0.192375	1.889002	0.174152
82	100.960	110.019	95.628	0.00606	0.000060	0.00351	0.00348	0.01053	0.02427	0.216	...	0.01751	0.04114	0.01237	20.536	0.554610	0.787896	-5.022288	0.146948	2.428306	0.264666
11	91.904	115.871	86.292	0.00540	0.000060	0.00281	0.00336	0.00844	0.02752	0.249	...	0.02214	0.04272	0.01141	21.414	0.583390	0.792520	-4.960234	0.363566	2.642476	0.275931
134	106.516	112.777	93.105	0.00589	0.000060	0.00291	0.00319	0.00873	0.04932	0.441	...	0.03651	0.08050	0.03031	17.060	0.637814	0.744064	-5.301321	0.320385	2.375138	0.243080
189	201.774	262.707	78.228	0.00694	0.000030	0.00412	0.00396	0.01235	0.02574	0.255	...	0.01758	0.04363	0.04441	19.368	0.508479	0.683761	-6.934474	0.159890	2.316346	0.112838
127	166.888	198.966	79.512	0.00638	0.000040	0.00368	0.00351	0.01104	0.02857	0.257	...	0.02301	0.04641	0.01796	18.330	0.585169	0.736964	-5.825257	0.115697	1.996146	0.196535
78	95.385	102.145	90.264	0.00608	0.000060	0.00331	0.00332	0.00994	0.03202	0.263	...	0.02455	0.05408	0.01062	21.875	0.644954	0.779612	-5.115212	0.249494	2.017753	0.260015
129	120.078	126.632	105.667	0.00270	0.000020	0.00116	0.00135	0.00349	0.01022	0.090	...	0.00903	0.01428	0.00487	26.369	0.491345	0.718839	-5.892061	0.195976	2.108873	0.183572
185	116.286	177.291	96.983	0.00314	0.000030	0.00134	0.00192	0.00403	0.01564	0.136	...	0.01691	0.02001	0.00737	24.199	0.598515	0.654331	-5.592584	0.133917	2.058658	0.214346
146	169.774	191.759	151.451	0.01568	0.000090	0.00863	0.00946	0.02589	0.08143	0.821	...	0.08808	0.11411	0.07530	12.359	0.561610	0.793509	-3.297668	0.414758	3.413649	0.457533
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
64	229.401	252.221	221.156	0.00205	0.000009	0.00114	0.00113	0.00342	0.01457	0.129	...	0.01016	0.02308	0.00300	26.415	0.276850	0.673636	-7.496264	0.056844	2.003032	0.073581
102	139.224	586.567	66.157	0.03011	0.000220	0.01854	0.01628	0.05563	0.09419	0.930	...	0.06023	0.16654	0.25930	10.489	0.596362	0.641418	-3.269487	0.270641	2.690917	0.444774
91	151.955	163.335	147.226	0.00419	0.000030	0.00224	0.00227	0.00672	0.07959	0.772	...	0.05690	0.13262	0.01658	19.664	0.501037	0.714360	-6.411497	0.207156	2.344876	0.134120
61	223.365	238.987	98.664	0.00264	0.000010	0.00154	0.00151	0.00461	0.01906	0.165	...	0.01340	0.03039	0.00301	26.138	0.447979	0.686264	-7.293801	0.086372	2.321560	0.098555
14	152.845	163.305	75.836	0.00294	0.000020	0.00121	0.00149	0.00364	0.01828	0.158	...	0.01246	0.03191	0.00609	24.922	0.474791	0.654027	-6.105098	0.203653	2.125618	0.170100
95	157.447	163.267	149.605	0.00369	0.000020	0.00201	0.00197	0.00602	0.03272	0.283	...	0.02571	0.05439	0.01018	21.693	0.447285	0.705658	-6.247076	0.180528	2.344348	0.164916
67	136.969	166.607	66.004	0.00923	0.000070	0.00507	0.00463	0.01520	0.03111	0.308	...	0.02603	0.04914	0.02659	19.979	0.498133	0.729067	-5.324574	0.205660	2.291558	0.226247
157	117.963	134.209	100.757	0.01813	0.000150	0.01117	0.00718	0.03351	0.04912	0.438	...	0.02916	0.07830	0.10748	19.075	0.630547	0.646786	-3.444478	0.303214	2.964568	0.261305
111	208.519	220.315	199.020	0.00609	0.000030	0.00368	0.00339	0.01105	0.01761	0.155	...	0.01307	0.02855	0.00830	22.407	0.338097	0.712466	-6.471427	0.184378	2.502336	0.136390
138	112.239	126.609	104.095	0.00472	0.000040	0.00238	0.00290	0.00715	0.05643	0.517	...	0.04451	0.09211	0.02629	17.366	0.640945	0.701404	-5.634576	0.306014	2.419253	0.209191
33	202.266	211.604	197.079	0.00180	0.000009	0.00093	0.00107	0.00278	0.00954	0.085	...	0.00719	0.01407	0.00072	32.684	0.368535	0.742133	-7.695734	0.178540	1.544609	0.056141
52	128.001	138.052	122.080	0.00436	0.000030	0.00137	0.00166	0.00411	0.02297	0.210	...	0.01677	0.03969	0.00481	24.692	0.459766	0.766204	-7.072419	0.220434	1.972297	0.119308
75	110.707	122.611	105.007	0.00516	0.000050	0.00277	0.00289	0.00831	0.02215	0.206	...	0.01715	0.03851	0.00472	25.197	0.463514	0.807217	-5.477592	0.315074	1.862092	0.228624
101	128.451	150.449	75.632	0.01551	0.000120	0.00905	0.00909	0.02716	0.06170	0.584	...	0.05174	0.09669	0.11843	15.060	0.639808	0.643327	-4.202730	0.310163	2.638279	0.356881
122	138.190	203.522	83.340	0.00704	0.000050	0.00406	0.00398	0.01218	0.04479	0.441	...	0.03220	0.07761	0.01968	18.305	0.538016	0.741480	-5.418787	0.160267	2.090438	0.229892
144	202.544	241.350	164.168	0.00254	0.000010	0.00100	0.00133	0.00301	0.02662	0.228	...	0.02006	0.04426	0.01049	20.680	0.497480	0.630409	-6.132663	0.220617	2.576563	0.159777
46	245.510	262.090	231.848	0.00235	0.000010	0.00127	0.00148	0.00380	0.01608	0.141	...	0.01149	0.02719	0.00476	24.602	0.467489	0.631653	-7.156076	0.127642	2.392122	0.097336
71	136.358	176.595	65.750	0.00971	0.000070	0.00534	0.00478	0.01601	0.04978	0.483	...	0.03736	0.08247	0.03361	18.570	0.543299	0.733232	-5.207985	0.224852	2.642276	0.242981
124	156.239	195.107	79.820	0.00694	0.000040	0.00403	0.00395	0.01209	0.02343	0.224	...	0.01720	0.03867	0.02020	19.196	0.618663	0.728421	-5.944191	0.143359	1.929715	0.181988
16	144.188	349.259	82.764	0.00544	0.000040	0.00211	0.00292	0.00632	0.02047	0.192	...	0.02074	0.02908	0.01859	22.333	0.567380	0.644692	-5.440040	0.239764	2.264501	0.218164
77	110.568	125.394	106.821	0.00462	0.000040	0.00226	0.00280	0.00677	0.02199	0.197	...	0.01636	0.03852	0.00420	25.820	0.429484	0.816340	-5.391029	0.250572	1.777901	0.232744
3	116.676	137.871	111.366	0.00997	0.000090	0.00502	0.00698	0.01505	0.05492	0.517	...	0.03772	0.08771	0.01353	20.644	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
86	178.222	202.450	141.047	0.00321	0.000020	0.00163	0.00194	0.00488	0.03759	0.327	...	0.02784	0.06219	0.03151	15.924	0.598714	0.712199	-6.366916	0.335753	2.654271	0.144614
19	156.405	189.398	142.822	0.00768	0.000050	0.00372	0.00399	0.01116	0.03995	0.348	...	0.04310	0.05164	0.03365	17.153	0.649554	0.686080	-4.554466	0.340176	2.856676	0.322111
68	143.533	162.215	65.809	0.01101	0.000080	0.00647	0.00467	0.01941	0.05384	0.478	...	0.03392	0.09455	0.04882	20.338	0.513237	0.731444	-5.869750	0.151814	2.118496	0.185580
54	108.807	134.656	102.874	0.00761	0.000070	0.00349	0.00486	0.01046	0.02719	0.255	...	0.02067	0.04450	0.01036	21.028	0.536009	0.819032	-4.649573	0.205558	1.986899	0.316700
130	120.289	128.143	100.209	0.00492	0.000040	0.00269	0.00238	0.00808	0.01412	0.125	...	0.01194	0.02110	0.01610	23.949	0.467160	0.724045	-6.135296	0.203630	2.539724	0.169923
104	154.003	160.267	128.621	0.00183	0.000010	0.00076	0.00100	0.00229	0.01030	0.094	...	0.00871	0.01406	0.00243	28.409	0.263654	0.691483	-7.111576	0.144780	2.065477	0.093193
187	116.342	581.289	94.246	0.00267	0.000020	0.00115	0.00148	0.00345	0.01300	0.117	...	0.01144	0.01892	0.00680	25.023	0.528485	0.663884	-6.359018	0.116636	2.152083	0.138868
22	167.930	193.221	79.068	0.00442	0.000030	0.00220	0.00247	0.00661	0.04351	0.377	...	0.04246	0.06685	0.01280	22.468	0.619060	0.679834	-4.330956	0.262384	2.916777	0.285695

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
131	120.256	125.306	104.773	0.00407	0.00003	0.00224	0.00205	0.00671	0.01516	0.138	...	0.01310	0.02164	0.01015	26.017	0.468621	0.735136	-6.112667	0.217013	2.527742	0.170633
60	209.144	237.494	109.379	0.00282	0.00001	0.00147	0.00152	0.00442	0.01861	0.170	...	0.01382	0.02925	0.00871	25.554	0.341788	0.678874	-7.040508	0.066994	2.460791	0.101516
53	129.336	139.867	118.604	0.00490	0.00004	0.00165	0.00183	0.00495	0.02498	0.228	...	0.01947	0.04188	0.00484	25.429	0.420383	0.785714	-6.836811	0.269866	2.223719	0.147491
136	113.400	133.344	107.816	0.00451	0.00004	0.00219	0.00283	0.00658	0.04879	0.431	...	0.04370	0.07154	0.02278	19.013	0.647900	0.708144	-4.378916	0.300067	2.445502	0.259451
181	148.462	161.078	141.998	0.00397	0.00003	0.00202	0.00235	0.00605	0.01831	0.163	...	0.01559	0.02849	0.00639	22.866	0.408598	0.768845	-5.704053	0.216204	2.679185	0.197710
94	157.821	172.975	68.401	0.00358	0.00002	0.00196	0.00196	0.00587	0.03716	0.307	...	0.02764	0.06185	0.00850	22.219	0.502380	0.712170	-6.251425	0.188056	2.143851	0.160812
135	110.453	127.611	105.554	0.00494	0.00004	0.00244	0.00315	0.00731	0.04128	0.379	...	0.03316	0.06688	0.02529	17.707	0.653427	0.706687	-5.333619	0.322044	2.631793	0.228319
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.01667	0.03804	0.10715	17.883	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
43	241.404	248.834	232.483	0.00281	0.00001	0.00157	0.00173	0.00470	0.01760	0.154	...	0.01251	0.03017	0.00675	23.145	0.457702	0.634267	-6.793547	0.158266	2.256699	0.117399
48	122.188	128.611	115.765	0.00524	0.00004	0.00169	0.00203	0.00507	0.01613	0.143	...	0.01433	0.02566	0.00839	23.162	0.579597	0.733659	-6.439398	0.266392	2.079922	0.133867
27	146.845	208.701	81.737	0.00496	0.00003	0.00250	0.00275	0.00749	0.01919	0.198	...	0.01826	0.02650	0.01328	25.119	0.358773	0.726652	-6.271690	0.196102	2.314209	0.162999
100	125.641	141.068	116.346	0.03316	0.00026	0.02144	0.01522	0.06433	0.09178	0.891	...	0.06196	0.16074	0.31482	8.867	0.671299	0.656846	-3.700544	0.260481	2.991063	0.370961
17	168.778	232.181	75.603	0.00718	0.00004	0.00284	0.00387	0.00853	0.03327	0.348	...	0.03430	0.04322	0.02919	20.376	0.631099	0.605417	-2.931070	0.434326	3.007463	0.430788
143	202.805	231.508	86.232	0.00370	0.00002	0.00189	0.00211	0.00568	0.01997	0.180	...	0.01506	0.03350	0.02010	18.687	0.536102	0.632631	-5.898673	0.213353	2.470746	0.189032
28	155.358	227.383	80.055	0.00310	0.00002	0.00159	0.00176	0.00476	0.01718	0.161	...	0.01661	0.02307	0.00677	25.970	0.470478	0.676258	-7.120925	0.279789	2.241742	0.108514
20	153.848	165.738	65.782	0.00840	0.00005	0.00428	0.00450	0.01285	0.03810	0.328	...	0.04055	0.05000	0.03871	17.536	0.660125	0.704087	-4.095442	0.262564	2.739710	0.365391
85	180.978	200.125	155.495	0.00406	0.00002	0.00220	0.00244	0.00659	0.03852	0.331	...	0.02877	0.06321	0.02782	16.176	0.583574	0.727747	-5.657899	0.315903	3.098256	0.200423
163	112.150	131.669	97.527	0.00519	0.00005	0.00291	0.00284	0.00873	0.01756	0.155	...	0.01363	0.02902	0.01435	21.219	0.557045	0.673086	-5.617124	0.184896	1.871871	0.212386
159	127.930	138.752	112.173	0.00605	0.00005	0.00321	0.00318	0.00962	0.03235	0.339	...	0.03105	0.04079	0.02663	19.651	0.654945	0.675865	-5.498456	0.234196	2.103014	0.216638
18	153.046	175.829	68.623	0.00742	0.00005	0.00364	0.00432	0.01092	0.05517	0.542	...	0.05767	0.07413	0.03160	17.280	0.665318	0.719467	-3.949079	0.357870	3.109010	0.377429
80	96.106	108.664	84.510	0.00694	0.00007	0.00389	0.00415	0.01168	0.04024	0.364	...	0.02876	0.06799	0.01823	19.055	0.544805	0.770466	-4.441519	0.155097	2.645959	0.327978
58	116.879	131.897	108.153	0.00788	0.00007	0.00334	0.00493	0.01003	0.02645	0.265	...	0.02137	0.04183	0.00786	22.603	0.540049	0.813432	-4.476755	0.262633	1.827012	0.326197
182	149.818	163.417	144.786	0.00336	0.00002	0.00174	0.00198	0.00521	0.02145	0.198	...	0.01666	0.03464	0.00595	23.008	0.329577	0.757180	-6.277170	0.109397	2.209021	0.156368
23	173.917	192.735	86.180	0.00476	0.00003	0.00221	0.00258	0.00663	0.04192	0.364	...	0.03772	0.06562	0.01840	20.422	0.537264	0.686894	-5.248776	0.210279	2.547508	0.253556
56	110.417	131.067	103.370	0.00784	0.00007	0.00352	0.00514	0.01056	0.03715	0.334	...	0.02802	0.06097	0.00969	21.422	0.541781	0.821364	-4.438453	0.238298	1.922940	0.335041
175	115.380	123.109	108.634	0.00332	0.00003	0.00160	0.00199	0.00480	0.01503	0.137	...	0.01133	0.02436	0.00401	26.005	0.405991	0.761255	-5.966779	0.197938	1.974857	0.184067
30	197.076	206.896	192.055	0.00289	0.00001	0.00166	0.00168	0.00498	0.01098	0.097	...	0.00802	0.01689	0.00339	26.775	0.422229	0.741367	-7.348300	0.177551	1.743867	0.085569
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.01588	0.03794	0.07223	19.020	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
36	177.876	192.921	168.013	0.00411	0.00002	0.00233	0.00241	0.00700	0.02126	0.189	...	0.01612	0.03463	0.00586	23.216	0.360148	0.778834	-6.149653	0.218037	2.477082	0.165827
167	260.105	264.919	237.303	0.00339	0.00001	0.00205	0.00186	0.00616	0.02030	0.197	...	0.01367	0.03557	0.00910	21.083	0.440988	0.628058	-7.517934	0.160414	1.881767	0.075587
105	149.689	160.368	133.608	0.00257	0.00002	0.00116	0.00134	0.00349	0.01346	0.126	...	0.01059	0.01979	0.00578	27.421	0.365488	0.719974	-6.997403	0.210279	1.994387	0.112878
24	163.656	200.841	76.779	0.00742	0.00005	0.00380	0.00390	0.01140	0.01659	0.164	...	0.01497	0.02214	0.01778	23.831	0.397937	0.732479	-5.557447	0.220890	2.692176	0.215961
179	148.143	155.982	135.041	0.00392	0.00003	0.00204	0.00231	0.00612	0.01450	0.131	...	0.01263	0.02175	0.00540	23.683	0.398499	0.778349	-5.711205	0.240875	2.845109	0.192730
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.04368	0.09403	0.01929	19.085	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
173	113.715	116.443	96.913	0.00349	0.00003	0.00171	0.00203	0.00514	0.01472	0.133	...	0.01148	0.02245	0.00478	26.547	0.380253	0.766700	-5.943501	0.192150	1.852542	0.179677
133	118.747	123.723	109.836	0.00331	0.00003	0.00168	0.00171	0.00504	0.01043	0.099	...	0.00903	0.01471	0.00504	25.619	0.482296	0.723096	-6.448134	0.178713	2.034827	0.141422
72	120.080	139.710	111.208	0.00405	0.00003	0.00180	0.00220	0.00540	0.01706	0.152	...	0.01345	0.02921	0.00442	25.742	0.495954	0.762959	-5.791820	0.329066	2.205024	0.188180
160	114.238	124.393	77.022	0.00581	0.00005	0.00299	0.00316	0.00896	0.04009	0.406	...	0.04114	0.04736	0.02073	20.437	0.653139	0.694571	-5.185987	0.259229	2.151121	0.244948
92	148.272	164.989	142.299	0.00459	0.00003	0.00250	0.00256	0.00750	0.04190	0.383	...	0.03051	0.07150	0.01914	18.780	0.454444	0.734504	-5.952058	0.087840	2.344336	0.186489
161	115.322	135.738	107.802	0.00619	0.00005	0.00352	0.00329	0.01057	0.03273	0.325	...	0.02931	0.04933	0.02810	19.388	0.577802	0.684373	-5.283009	0.226528	2.442906	0.238281
170	244.990	272.210	239.170	0.00451	0.00002	0.00279	0.00237	0.00837	0.01897	0.181	...	0.01255	0.03253	0.01049	21.528	0.522812	0.646818	-7.304500	0.171088	2.095237	0.096220
140	170.368	268.796	79.543	0.00571	0.00003	0.00232	0.00269	0.00696	0.03273	0.281	...	0.02824	0.05139	0.02485	18.540	0.677131	0.685057	-4.796845	0.397749	2.963799	0.277227
116	158.219	442.557	71.948	0.00476	0.00003	0.00214	0.00207	0.00642	0.01458	0.148	...	0.01312	0.01818	0.01554	26.356	0.450798	0.653823	-6.051233	0.273280	2.640798	0.170106
96	159.116	168.913	144.811	0.00342	0.00002	0.00178	0.00184	0.00535	0.03381	0.307	...	0.02809	0.05417	0.00852	22.663	0.366329	0.693429	-6.417440	0.194627	2.473239	0.151709
142	198.458	219.290	148.691	0.00376	0.00002	0.00182	0.00215	0.00546	0.03527	0.297	...	0.02530	0.06165	0.01728	18.702	0.606273	0.661735	-5.585259	0.310746	2.465528	0.209863
162	114.554	126.778	91.121	0.00651	0.00006	0.00366	0.00340	0.01097	0.03658	0.369	...	0.03091	0.05592	0.02707	18.954	0.685151	0.719576	-5.529833	0.242750	2.408689	0.220520
153	121.345	139.644	98.250	0.00684	0.00006	0.00388	0.00332	0.01164	0.02534	0.241	...	0.02056	0.04019	0.04179	21.520	0.566867	0.670475	-4.865194	0.246404	2.013530	0.168581
119	217.116	233.481	93.978	0.00404	0.00002	0.00127	0.00128	0.00381	0.01299	0.124	...	0.01075	0.02038	0.00681	24.581	0.462516	0.582710	-5.517173	0.389295	2.925862	0.220657
32	198.383	215.203	193.104	0.00212	0.00001	0.00113	0.00135	0.00339	0.01263	0.111	...	0.00951	0.01919	0.00119	30.775	0.465946	0.738703	-7.067931	0.175181	1.512275	0.096320
184	116.848	217.552	99.503	0.00531	0.00005	0.00260	0.00346	0.00780	0.01795	0.163	...	0.01756	0.02429	0.01179	22.085	0.663842	0.656516	-5.198864	0.206768	2.120412	0.252404
59	114.847	271.314	104.680	0.00867	0.00008	0.00373	0.00520	0.01120	0.03225	0.350	...	0.02519	0.05414	0.01143	21.660	0.547975	0.817396	-4.609161	0.221711	1.831691	0.316395
112	204.664	221.300	189.621	0.00841	0.00004	0.00502	0.00485	0.01506	0.02378	0.210	...	0.01767	0.03831	0.01316	21.305	0.498877	0.722085	-4.876336	0.212054	2.376749	0.268144
126	138.145	197.238	81.114	0.00544	0.00004	0.00294	0.00327	0.00883	0.02791	0.246	...	0.02259	0.04451	0.01794	18.178	0.623209	0.738245	-5.540351	0.087165	1.821297	0.214075
188	114.563	119.167	86.647	0.00327	0.00003	0.00146	0.00184	0.00439	0.01185	0.106	...	0.01095	0.01672	0.00703	24.775	0.555303	0.659132	-6.710219	0.149694	1.913990	0.121777
117	170.756	450.247	79.032	0.00555	0.00003	0.00244	0.00261	0.00731	0.01725	0.175	...	0.01652	0.02270	0.01802	25.690	0.486738	0.676023	-4.597834	0.372114	2.975889	0.282780
25	104.400	206.002	77.968	0.00633	0.00006	0.00316	0.00375	0.00948	0.03767	0.381	...	0.03780	0.05197	0.02887	22.066	0.522746	0.737948	-5.571843	0.236853	2.846369	0.219514
90	166.605	206.008	78.032	0.00742	0.00004	0.00387	0.00453	0.01161	0.06640	0.634	...	0.05114	0.10949	0.08725	11.744	0.653410	0.733165	-4.508984	0.389232	3.317586	0.301952
141	208.083	253.792	91.802	0.00757	0.00004	0.00428	0.00428	0.01285	0.06725	0.571	...	0.04464	0.12047	0.04238	15.648	0.606344	0.665945	-5.410336	0.288917	2.665133	0.231723
137	113.166	130.270	100.673	0.00502	0.00004	0.00257	0.00312	0.00772	0.05279	0.476	...	0.04134	0.08689	0.03690	16.747	0.625362	0.708617	-4.654894	0.304107	2.672362	0.274387